Data mining is the process of discovering useful patterns in data. Patterns in the mined data may be initially hidden and unknown, and could include useful information such as event frequency, magnitude, duration, and/or cost. Data mining draws from several fields, including but not limited to machine learning, statistics, and database design. Techniques used in data mining may include clustering, associative rules, visualization, and probabilistic graphical dependency models, all of which may be used to identify the hidden and potentially useful data that is often distributed across multiple and heterogeneous databases in a manufacturing environment.
Temporal data mining (TDM) is a particular branch of data mining. TDM refers to the application of data mining concepts to find patterns in a time-based or temporal stream of data. There are four components of any TDM method: sampling of the data, i.e., time sampling, encoding or embedding of the data, extracting temporal patterns from the data, and then learning the extracted temporal patterns using a machine learning model or other techniques.